Reinforcement Learning: How Does It Work?
Reinforcement learning is a type of machine learning where we use something we call an agent. Furthermore, we place this agent in a certain environment with which it’s going to interact.
As it’s interacting with the envirnoment, it pushes itself toward a certain goal. Therefore we need to use a reward system, which either rewards or punishes the agent, depending on if agent moves toward a goal or not.
This approach can be especially powerful when we use it with deep learning. In such cases the agent could learn from high-dimensional data like images or videos.
Deep Reinforcement Learning Applications
Gameplay
One of the most known applications of deep reinforcement learning is in the field of game playing. This is where we train our agents as players in the game environment.
For example, in a player versus player games, we pit these agents against each other where they learn in a sped up gameplay. So if a game normally takes 30 minutes in normal time speed with humans, for these algorithms it could take seconds. Therefore, these agents have been able to achieve breakthrough performance.
The reason for that is mainly because they’re able to learn fast and making use of sophisticated neural network architectures.
Robotics
This kind of algorithms are also useful in other fields such as robotics. This is where robots can be trained to perform tasks like grasping and manipulation.
Control Systems
They’re also useful for optimizing control systems such as power grids and traffic networks.
Another state of the art application of these algorithms is in the field of autonomous vehicles. This is where we can use it to train self-driving cars to navigate in a complex and dynamic environment. Furthermore, by using all sorts of sensors they can adapt to traffic and make decisions in real time.
Deep reinforcement learning is a rapidly developing field, where new applications arise all the time. However, there is also a downside to it, which is unstable learning process. Researchers are actively working on new techniques to overcome this challenge to make reinforcement learning more widely applicable.
Conclusion
In conclusion, reinforcement learning, deep or otherwise are powerful techniques we can use to achieve breakthrough performance in a variety of fields. While there are still challenges that we need to address, there is still a big potential in these algorithms.
I hope this article helped you gain a better understanding of reinforcement learning and maybe even inspire you to learn even more.
